"""
Utilities for extensions of and operations on Python collections.
"""

import io
import itertools
from collections import OrderedDict, defaultdict
from collections.abc import Iterator as IteratorABC
from collections.abc import Sequence
from dataclasses import fields, is_dataclass
from enum import Enum, auto
from typing import (
    Any,
    Callable,
    Dict,
    Generator,
    Hashable,
    Iterable,
    Iterator,
    List,
    Optional,
    Set,
    Tuple,
    Type,
    TypeVar,
    Union,
    cast,
)
from unittest.mock import Mock

from prefect._internal.pydantic import HAS_PYDANTIC_V2

if HAS_PYDANTIC_V2:
    import pydantic.v1 as pydantic
else:
    import pydantic

# Quote moved to `prefect.utilities.annotations` but preserved here for compatibility
from prefect.utilities.annotations import BaseAnnotation, Quote, quote  # noqa


class AutoEnum(str, Enum):
    """
    An enum class that automatically generates value from variable names.

    This guards against common errors where variable names are updated but values are
    not.

    In addition, because AutoEnums inherit from `str`, they are automatically
    JSON-serializable.

    See https://docs.python.org/3/library/enum.html#using-automatic-values

    Example:
        ```python
        class MyEnum(AutoEnum):
            RED = AutoEnum.auto() # equivalent to RED = 'RED'
            BLUE = AutoEnum.auto() # equivalent to BLUE = 'BLUE'
        ```
    """

    def _generate_next_value_(name, start, count, last_values):
        return name

    @staticmethod
    def auto():
        """
        Exposes `enum.auto()` to avoid requiring a second import to use `AutoEnum`
        """
        return auto()

    def __repr__(self) -> str:
        return f"{type(self).__name__}.{self.value}"


KT = TypeVar("KT")
VT = TypeVar("VT")


def dict_to_flatdict(
    dct: Dict[KT, Union[Any, Dict[KT, Any]]], _parent: Tuple[KT, ...] = None
) -> Dict[Tuple[KT, ...], Any]:
    """Converts a (nested) dictionary to a flattened representation.

    Each key of the flat dict will be a CompoundKey tuple containing the "chain of keys"
    for the corresponding value.

    Args:
        dct (dict): The dictionary to flatten
        _parent (Tuple, optional): The current parent for recursion

    Returns:
        A flattened dict of the same type as dct
    """
    typ = cast(Type[Dict[Tuple[KT, ...], Any]], type(dct))
    items: List[Tuple[Tuple[KT, ...], Any]] = []
    parent = _parent or tuple()

    for k, v in dct.items():
        k_parent = tuple(parent + (k,))
        # if v is a non-empty dict, recurse
        if isinstance(v, dict) and v:
            items.extend(dict_to_flatdict(v, _parent=k_parent).items())
        else:
            items.append((k_parent, v))
    return typ(items)


def flatdict_to_dict(
    dct: Dict[Tuple[KT, ...], VT],
) -> Dict[KT, Union[VT, Dict[KT, VT]]]:
    """Converts a flattened dictionary back to a nested dictionary.

    Args:
        dct (dict): The dictionary to be nested. Each key should be a tuple of keys
            as generated by `dict_to_flatdict`

    Returns
        A nested dict of the same type as dct
    """
    typ = type(dct)
    result = cast(Dict[KT, Union[VT, Dict[KT, VT]]], typ())
    for key_tuple, value in dct.items():
        current_dict = result
        for prefix_key in key_tuple[:-1]:
            # Build nested dictionaries up for the current key tuple
            # Use `setdefault` in case the nested dict has already been created
            current_dict = current_dict.setdefault(prefix_key, typ())  # type: ignore
        # Set the value
        current_dict[key_tuple[-1]] = value

    return result


T = TypeVar("T")


def isiterable(obj: Any) -> bool:
    """
    Return a boolean indicating if an object is iterable.

    Excludes types that are iterable but typically used as singletons:
    - str
    - bytes
    - IO objects
    """
    try:
        iter(obj)
    except TypeError:
        return False
    else:
        return not isinstance(obj, (str, bytes, io.IOBase))


def ensure_iterable(obj: Union[T, Iterable[T]]) -> Iterable[T]:
    if isinstance(obj, Sequence) or isinstance(obj, Set):
        return obj
    obj = cast(T, obj)  # No longer in the iterable case
    return [obj]


def listrepr(objs: Iterable[Any], sep: str = " ") -> str:
    return sep.join(repr(obj) for obj in objs)


def extract_instances(
    objects: Iterable,
    types: Union[Type[T], Tuple[Type[T], ...]] = object,
) -> Union[List[T], Dict[Type[T], T]]:
    """
    Extract objects from a file and returns a dict of type -> instances

    Args:
        objects: An iterable of objects
        types: A type or tuple of types to extract, defaults to all objects

    Returns:
        If a single type is given: a list of instances of that type
        If a tuple of types is given: a mapping of type to a list of instances
    """
    types = ensure_iterable(types)

    # Create a mapping of type -> instance from the exec values
    ret = defaultdict(list)

    for o in objects:
        # We iterate here so that the key is the passed type rather than type(o)
        for type_ in types:
            if isinstance(o, type_):
                ret[type_].append(o)

    if len(types) == 1:
        return ret[types[0]]

    return ret


def batched_iterable(iterable: Iterable[T], size: int) -> Iterator[Tuple[T, ...]]:
    """
    Yield batches of a certain size from an iterable

    Args:
        iterable (Iterable): An iterable
        size (int): The batch size to return

    Yields:
        tuple: A batch of the iterable
    """
    it = iter(iterable)
    while True:
        batch = tuple(itertools.islice(it, size))
        if not batch:
            break
        yield batch


class StopVisiting(BaseException):
    """
    A special exception used to stop recursive visits in `visit_collection`.

    When raised, the expression is returned without modification and recursive visits
    in that path will end.
    """


def visit_collection(
    expr,
    visit_fn: Callable[[Any], Any],
    return_data: bool = False,
    max_depth: int = -1,
    context: Optional[dict] = None,
    remove_annotations: bool = False,
):
    """
    This function visits every element of an arbitrary Python collection. If an element
    is a Python collection, it will be visited recursively. If an element is not a
    collection, `visit_fn` will be called with the element. The return value of
    `visit_fn` can be used to alter the element if `return_data` is set.

    Note that when using `return_data` a copy of each collection is created to avoid
    mutating the original object. This may have significant performance penalties and
    should only be used if you intend to transform the collection.

    Supported types:
    - List
    - Tuple
    - Set
    - Dict (note: keys are also visited recursively)
    - Dataclass
    - Pydantic model
    - Prefect annotations

    Args:
        expr (Any): a Python object or expression
        visit_fn (Callable[[Any], Awaitable[Any]]): an async function that
            will be applied to every non-collection element of expr.
        return_data (bool): if `True`, a copy of `expr` containing data modified
            by `visit_fn` will be returned. This is slower than `return_data=False`
            (the default).
        max_depth: Controls the depth of recursive visitation. If set to zero, no
            recursion will occur. If set to a positive integer N, visitation will only
            descend to N layers deep. If set to any negative integer, no limit will be
            enforced and recursion will continue until terminal items are reached. By
            default, recursion is unlimited.
        context: An optional dictionary. If passed, the context will be sent to each
            call to the `visit_fn`. The context can be mutated by each visitor and will
            be available for later visits to expressions at the given depth. Values
            will not be available "up" a level from a given expression.

            The context will be automatically populated with an 'annotation' key when
            visiting collections within a `BaseAnnotation` type. This requires the
            caller to pass `context={}` and will not be activated by default.
        remove_annotations: If set, annotations will be replaced by their contents. By
            default, annotations are preserved but their contents are visited.
    """

    def visit_nested(expr):
        # Utility for a recursive call, preserving options and updating the depth.
        return visit_collection(
            expr,
            visit_fn=visit_fn,
            return_data=return_data,
            remove_annotations=remove_annotations,
            max_depth=max_depth - 1,
            # Copy the context on nested calls so it does not "propagate up"
            context=context.copy() if context is not None else None,
        )

    def visit_expression(expr):
        if context is not None:
            return visit_fn(expr, context)
        else:
            return visit_fn(expr)

    # Visit every expression
    try:
        result = visit_expression(expr)
    except StopVisiting:
        max_depth = 0
        result = expr

    if return_data:
        # Only mutate the expression while returning data, otherwise it could be null
        expr = result

    # Then, visit every child of the expression recursively

    # If we have reached the maximum depth, do not perform any recursion
    if max_depth == 0:
        return result if return_data else None

    # Get the expression type; treat iterators like lists
    typ = list if isinstance(expr, IteratorABC) and isiterable(expr) else type(expr)
    typ = cast(type, typ)  # mypy treats this as 'object' otherwise and complains

    # Then visit every item in the expression if it is a collection
    if isinstance(expr, Mock):
        # Do not attempt to recurse into mock objects
        result = expr

    elif isinstance(expr, BaseAnnotation):
        if context is not None:
            context["annotation"] = expr
        value = visit_nested(expr.unwrap())

        if remove_annotations:
            result = value if return_data else None
        else:
            result = expr.rewrap(value) if return_data else None

    elif typ in (list, tuple, set):
        items = [visit_nested(o) for o in expr]
        result = typ(items) if return_data else None

    elif typ in (dict, OrderedDict):
        assert isinstance(expr, (dict, OrderedDict))  # typecheck assertion
        items = [(visit_nested(k), visit_nested(v)) for k, v in expr.items()]
        result = typ(items) if return_data else None

    elif is_dataclass(expr) and not isinstance(expr, type):
        values = [visit_nested(getattr(expr, f.name)) for f in fields(expr)]
        items = {field.name: value for field, value in zip(fields(expr), values)}
        result = typ(**items) if return_data else None

    elif isinstance(expr, pydantic.BaseModel):
        # NOTE: This implementation *does not* traverse private attributes
        # Pydantic does not expose extras in `__fields__` so we use `__fields_set__`
        # as well to get all of the relevant attributes
        # Check for presence of attrs even if they're in the field set due to pydantic#4916
        model_fields = {
            f for f in expr.__fields_set__.union(expr.__fields__) if hasattr(expr, f)
        }
        items = [visit_nested(getattr(expr, key)) for key in model_fields]

        if return_data:
            # Collect fields with aliases so reconstruction can use the correct field name
            aliases = {
                key: value.alias
                for key, value in expr.__fields__.items()
                if value.has_alias
            }

            model_instance = typ(
                **{
                    aliases.get(key) or key: value
                    for key, value in zip(model_fields, items)
                }
            )

            # Private attributes are not included in `__fields_set__` but we do not want
            # to drop them from the model so we restore them after constructing a new
            # model
            for attr in expr.__private_attributes__:
                # Use `object.__setattr__` to avoid errors on immutable models
                object.__setattr__(model_instance, attr, getattr(expr, attr))

            # Preserve data about which fields were explicitly set on the original model
            object.__setattr__(model_instance, "__fields_set__", expr.__fields_set__)
            result = model_instance
        else:
            result = None

    else:
        result = result if return_data else None

    return result


def remove_nested_keys(keys_to_remove: List[Hashable], obj):
    """
    Recurses a dictionary returns a copy without all keys that match an entry in
    `key_to_remove`. Return `obj` unchanged if not a dictionary.

    Args:
        keys_to_remove: A list of keys to remove from obj obj: The object to remove keys
            from.

    Returns:
        `obj` without keys matching an entry in `keys_to_remove` if `obj` is a
            dictionary. `obj` if `obj` is not a dictionary.
    """
    if not isinstance(obj, dict):
        return obj
    return {
        key: remove_nested_keys(keys_to_remove, value)
        for key, value in obj.items()
        if key not in keys_to_remove
    }


def distinct(
    iterable: Iterable[T],
    key: Callable[[T], Any] = (lambda i: i),
) -> Generator[T, None, None]:
    seen: Set = set()
    for item in iterable:
        if key(item) in seen:
            continue
        seen.add(key(item))
        yield item


def get_from_dict(dct: Dict, keys: Union[str, List[str]], default: Any = None) -> Any:
    """
    Fetch a value from a nested dictionary or list using a sequence of keys.

    This function allows to fetch a value from a deeply nested structure
    of dictionaries and lists using either a dot-separated string or a list
    of keys. If a requested key does not exist, the function returns the
    provided default value.

    Args:
        dct: The nested dictionary or list from which to fetch the value.
        keys: The sequence of keys to use for access. Can be a
            dot-separated string or a list of keys. List indices can be included
            in the sequence as either integer keys or as string indices in square
            brackets.
        default: The default value to return if the requested key path does not
            exist. Defaults to None.

    Returns:
        The fetched value if the key exists, or the default value if it does not.

    Examples:
    >>> get_from_dict({'a': {'b': {'c': [1, 2, 3, 4]}}}, 'a.b.c[1]')
    2
    >>> get_from_dict({'a': {'b': [0, {'c': [1, 2]}]}}, ['a', 'b', 1, 'c', 1])
    2
    >>> get_from_dict({'a': {'b': [0, {'c': [1, 2]}]}}, 'a.b.1.c.2', 'default')
    'default'
    """
    if isinstance(keys, str):
        keys = keys.replace("[", ".").replace("]", "").split(".")
    try:
        for key in keys:
            try:
                # Try to cast to int to handle list indices
                key = int(key)
            except ValueError:
                # If it's not an int, use the key as-is
                # for dict lookup
                pass
            dct = dct[key]
        return dct
    except (TypeError, KeyError, IndexError):
        return default
